Virtual visual sensors and their application in structural health monitoring

Abstract

Wireless sensor networks are being increasingly accepted as an effective tool for structural health monitoring. The ability to deploy a wireless array of sensors efficiently and effectively is a key factor in structural health monitoring. Sensor installation and management can be difficult in practice for a variety of reasons: a hostile environment, high labour costs and bandwidth limitations. We present and evaluate a proof-of-concept application of virtual visual sensors to the wellknown engineering problem of the cantilever beam, as a convenient physical sensor substitute for certain problems and environments. We demonstrate the effectiveness of virtual visual sensors as a means to achieve non-destructive evaluation. Major benefits of virtual visual sensors are its non-invasive nature, ease of installation and cost-effectiveness. The novelty of virtual visual sensors lies in the combination of marker extraction with visual tracking realised by modern computer vision algorithms. We demonstrate that by deploying a collection of virtual visual sensors on an oscillating structure, its modal shapes and frequencies can be readily extracted from a sequence of video images. Subsequently, we perform damage detection and localisation by means of a wavelet-based analysis. The contributions of this article are as follows: (1) use of a sub-pixel accuracy marker extraction algorithm to construct virtual sensors in the spatial domain, (2) embedding dynamic marker linking within a tracking-by-correspondence paradigm that offers benefits in computational efficiency and registration accuracy over traditional tracking-by-searching systems and (3) validation of virtual visual sensors in the context of a structural health monitoring application.

Monitoring of Structures and Mechanical Systems Using Virtual Visual Sensors for Video Analysis: Fundamental Concept and Proof of Feasibility

“…The term “VVS” follows the terminology suggested by Song, Bowen et al [10]. Although the approach presented in the latter paper may appear similar, it is fundamentally different as they were employing a Lagrangian specification where a target (or feature) is tracked in space and time.…”

Eulerian-based virtual visual sensors to detect natural frequencies of structures

“…Digital video cameras in conjunction with image processing techniques have also been used to this aim and other SHM purposes as they offer an inexpensive yet promising alternative. Digital image correlation (DIC) techniques along with other matching algorithms have been employed to monitor displacements with video cameras or tracking certain targets through time [10,16,17], [18]. Zaurin and Catbas [19][20][21] applied image processing techniques to use video cameras as loading sensors for bridges and defined a so-called unit influence line (UIL) index as a measure of health in bridges [19].…”

Review of machine-vision based methodologies for displacement measurement in civil structures

“…Song et al [74] proposed a target tracking method based on circular Hough transform for marker detection and coherent point drift algorithm for marker matching and the method was applied for system identification of a steel cantilever beam in the laboratory. Field applications include Khuc and Catbas [22,75] who applied the FREAK and SIFT methods for deformation measurement in a stadium structure and a railway bridge and Ehrhart and Lienhart [59,64] who applied the ORB method for deformation measurement in a short-span footbridge.…”